Classification of Incomplete Data Using the Fuzzy ARTMAP Neural Network

The fuzzy ARTMAP neural network is used to classify data that is incomplete in one or more ways. These include a limited number of training cases, missing components, missing class labels, and missing classes. Modifications for dealing with such incomplete data are introduced, and performance is assessed on an emitter identification task using a data base of radar pulsesDefense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409) (S.G. and M.A.R); National Science Foundation (IRI-97-20333) (S.G.); Natural Sciences and Engineerging Research Council of Canada (E.G.); Office of Naval Research (N00014-95-1-0657

Familiarity Discrimination of Radar Pulses

The ARTMAP-FD neural network performs both identification (placing test patterns in classes encountered during training) and familiarity discrimination (judging whether a test pattern belongs to any of the classes encountered during training). The performance of ARTMAP-FD is tested on radar pulse data obtained in the field, and compared to that of the nearest-neighbor-based NEN algorithm and to a k > 1 extension of NEN

Knowing an “Educational Institution” When You See One: Applying the Commerciality Approach to Tax Exemptions for Universities Under § 501(C)(3)

Throughout American history, colleges and universities have had a constant presence in the nation. Education has consequently played a paramount role in the growth and development of the country, which the government has been determined to foster. Under Internal Revenue Code § 501(c)(3), the government provides tax exemptions for non-profit organizations, including educational institutions, such as colleges. Yet, the size and scope of these institutions has changed dramatically since the earliest colonial schools. With tremendous campuses, massive sporting complexes, and lucrative research contracts, modern universities bear little resemblance to the small private schools they were at their inception and undertake many endeavors outside the realm of pure academia. Thus, it seems that universities in their modern form are no longer purely educational institutions, but instead appear to be more akin to businesses. This Note will argue that tax exemptions for modern universities as educational institutions are no longer appropriate given the extensive capabilities of the entities. The history and purpose of the tax code suggest that the use of the tax exemption was to foster education and its continued use for profit-making endeavors seems to undermine that intention. This Note will also outline numerous methods for defining what constitutes an educational institution, arguing that the commerciality approach represents the most effective means to resolve this tension and providing proposals to implement this framework

Comparison of Classifiers for Radar Emitter Type Identification

ARTMAP neural network classifiers are considered for the identification of radar emitter types from their waveform parameters. These classifiers can represent radar emitter type classes with one or more prototypes, perform on-line incremental learning to account for novelty encountered in the field, and process radar pulse streams at high speed, making them attractive for real-time applications such as electronic support measures (ESM). The performance of four ARTMAP variants- ARTMAP (Stage 1), ARTMAP-IC, fuzzy ARTMAP and Gaussian ARTMAP - is assessed with radar data gathered in the field. The k nearest neighbor (kNN) and radial basis function (RDF) classifiers are used for reference. Simulation results indicate that fuzzy ARTMAP and Gaussian ARTMAP achieve an average classification rate consistently higher than that of the other ARTMAP classifers and comparable to that of kNN and RBF. ART-EMAP, ARTMAP-IC and fuzzy ARTMAP require fewer training epochs than Gaussian ARTMAP and RBF, and substantially fewer prototype vectors (thus, smaller physical memory requirements and faster fielded performance) than Gaussian ARTMAP, RBF and kNN. Overall, fuzzy ART MAP performs at least as well as the other classifiers in both accuracy and computational complexity, and better than each of them in at least one of these aspects of performance. Incorporation into fuzzy ARTMAP of the MT- feature of ARTMAP-IC is found to be essential for convergence during on-line training with this data set.Defense Advanced Research Projects Agency and the Office of Naval Research (N000I4-95-1-409 (S.G. and M.A.R.); National Science Foundation (IRI-97-20333) (S.G.); Natural Science and Engineering Research Council of Canada (E.G.); Office of Naval Research (N00014-95-1-0657

Intelligent Word Embeddings of Free-Text Radiology Reports

Radiology reports are a rich resource for advancing deep learning applications in medicine by leveraging the large volume of data continuously being updated, integrated, and shared. However, there are significant challenges as well, largely due to the ambiguity and subtlety of natural language. We propose a hybrid strategy that combines semantic-dictionary mapping and word2vec modeling for creating dense vector embeddings of free-text radiology reports. Our method leverages the benefits of both semantic-dictionary mapping as well as unsupervised learning. Using the vector representation, we automatically classify the radiology reports into three classes denoting confidence in the diagnosis of intracranial hemorrhage by the interpreting radiologist. We performed experiments with varying hyperparameter settings of the word embeddings and a range of different classifiers. Best performance achieved was a weighted precision of 88% and weighted recall of 90%. Our work offers the potential to leverage unstructured electronic health record data by allowing direct analysis of narrative clinical notes.Comment: AMIA Annual Symposium 201

Paths to Fisheries Subsidies Reform: Creating Sustainable Fisheries Through Trade and Economics

The world depends on the oceans for food and livelihood. More than a billion people worldwide depend on fish as a source of protein, including some of the poorest populations on earth. According to the United Nations Food and Agriculture Organization (FAO), the world must produce 70 percent more food to meet coming hunger needs.Fishing activities support coastal communities and hundreds of millions of people who depend on fishing for all or part of their income. Of the world's fishers, more than 95 percent engage in small-scale and artisanal activity and catch nearly the same amount of fish for human consumption as the highly capitalized industrial sector. Small-scale and artisanal fishing produces a greater return than industrial operations by unit of input, investment in catch, and number of people employed.Today, overfishing and other destructive fishing practices have severely decreased the world's fish populations. The FAO estimates that 90 percent of marine fisheries worldwide are now overexploited, fully exploited, significantly depleted, or recovering from overexploitation

Host transcription in active and latent tuberculosis

A recent study has identified a transcriptional signature for active tuberculosis, suggesting that the distinction between active and latent forms may not be absolute